
import sys
sys.path.append('..')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import datetime
import scipy.stats as stats
from descartes import PolygonPatch
import statsmodels.api as sm

from windrose import WindroseAxes
from windrose import WindAxes
from windrose import plot_windrose
import matplotlib.cm as cm

# Use seaborn style defaults and set default figure size
plt.style.use('seaborn-pastel')
sns.set_theme(style="whitegrid")


from functions import dplot, qqplot, rplot, stat_calc


dfnov = pd.read_csv('./data/met_data_nov_utc.csv')
dfdec = pd.read_csv('./data/met_data_dec_utc.csv')
dfjan = pd.read_csv('./data/met_data_jan_utc.csv')


dfnov.head()


dfdec.head()


dfjan.head()


dfnov['datetime'] = pd.to_datetime(dfnov.date) + pd.to_timedelta(dfnov.hour)
dfdec['datetime'] = pd.to_datetime(dfdec.time)
dfjan['datetime'] = pd.to_datetime(dfjan.datetime)


dfnov = dfnov.set_index(dfnov.datetime)
dfdec = dfdec.set_index(dfdec.datetime)
dfjan = dfjan.set_index(dfjan.datetime)

dfnov.drop(['date', 'hour', 'datetime'], axis=1, inplace=True)
dfdec.drop(['time', 'datetime'], axis=1, inplace=True)
dfjan.drop(['datetime'], axis=1, inplace=True)


# Add the missing values of datetime in November and December as NaN

idx_nov = pd.date_range('2020-11-01 00:00:00','2020-11-30 23:00:00', freq='H')
idx_dec = pd.date_range('2020-12-01 00:00:00','2020-12-31 23:00:00', freq='H')

dfnov = dfnov.reindex(idx_nov, fill_value=np.nan)
dfdec = dfdec.reindex(idx_dec, fill_value=np.nan)


dfnov.loc["2020-11-03 9:00:00"]

mod_temp        30.508610
obs_temp        28.814440
mod_wd                NaN
obs_wd         308.138900
mod_ws           1.757600
obs_ws           4.513278
mod_swdown       0.000000
obs_swlower     75.140000
obs_swupper    452.300000
Name: 2020-11-03 09:00:00, dtype: float64


dfnov.loc[dfnov.index.hour == 9, 'mod_swdown'] = np.nan
dfnov.loc[dfnov.index.hour == 9, 'mod_ws'] = np.nan
dfnov.loc[dfnov.index.hour == 9, 'mod_wd'] = np.nan

dfdec.loc[dfdec.index.hour == 9, 'mod_swdown'] = np.nan
dfdec.loc[dfdec.index.hour == 9, 'mod_ws'] = np.nan
dfdec.loc[dfdec.index.hour == 9, 'mod_wd'] = np.nan

dfjan.loc[dfjan.index.hour == 9, 'mod_swdown'] = np.nan
dfjan.loc[dfjan.index.hour == 9, 'mod_ws'] = np.nan
dfjan.loc[dfjan.index.hour == 9, 'mod_wd'] = np.nan


### Interpolate the data

# dfnov = dfnov.interpolate(method='cubic')
# dfdec = dfdec.interpolate(method='cubic')
# dfjan = dfjan.interpolate(method='cubic')


df = pd.concat([dfnov, dfdec, dfjan], axis=0)


df.shape

(2208, 11)


# Plot of Temperature (2m) for Nov, Dec, Jan

fig = plt.figure(figsize=(20,15))
plt.subplot(311)
plt.plot(dfnov.index, dfnov['mod_temp'], label='model', color='red')
plt.plot(dfnov.index, dfnov['mod_temp'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfnov.index, dfnov['obs_temp'], label='observed', color='blue')
plt.plot(dfnov.index, dfnov['obs_temp'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('2$m$ Temperature (in $^{o}C$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('November 2020', fontsize=18)

plt.subplot(312)
plt.plot(dfdec.index, dfdec['mod_temp'], label='model', color='red')
plt.plot(dfdec.index, dfdec['mod_temp'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfdec.index, dfdec['obs_temp'], label='observed', color='blue')
plt.plot(dfdec.index, dfdec['obs_temp'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('2$m$ Temperature (in $^{o}C$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('December 2020', fontsize=18)

plt.subplot(313)
plt.plot(dfjan.index, dfjan['mod_temp'], label='model', color='red')
plt.plot(dfjan.index, dfjan['mod_temp'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfjan.index, dfjan['obs_temp'], label='observed', color='blue')
plt.plot(dfjan.index, dfjan['obs_temp'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('2$m$ Temperature (in $^{o}C$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('January 2021', fontsize=18)

fig.suptitle('2$m$ Temperature in Delhi', fontsize=24, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/temp.png')


# Diurnal variation

fig = plt.figure(figsize=(15,15))

plt.subplot(411)
dplot.plot(df, df, 'mod_temp', 'obs_temp')
plt.ylabel('Temperature', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('NDJ Combined', fontsize=18)

plt.subplot(412)
dplot.plot(dfnov, dfnov, 'mod_temp', 'obs_temp')
plt.ylabel('Temperature', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('November 2020', fontsize=18)

plt.subplot(413)
dplot.plot(dfdec, dfdec, 'mod_temp', 'obs_temp')
plt.ylabel('Temperature', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('December 2020', fontsize=18)

plt.subplot(414)
dplot.plot(dfjan, dfjan, 'mod_temp', 'obs_temp')
plt.ylabel('Temperature', fontsize=14)
plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('January 2021', fontsize=18)

fig.suptitle('Temperature at 2$m$ (in $^{o}C$) over Delhi', fontsize=24, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/diurnal_temp.png')


# Calculation of WS and WD for Nov, Dec, Jan

dfnov['mod_zonal_wind'] = dfnov['mod_ws'] * (np.sin((np.pi/180)*dfnov['mod_wd']))
dfnov['mod_meridional_wind'] = dfnov['mod_ws'] * (np.cos((np.pi/180)*dfnov['mod_wd']))
dfnov['obs_zonal_wind'] = dfnov['obs_ws'] * (np.sin((np.pi/180)*dfnov['obs_wd']))
dfnov['obs_meridional_wind'] = dfnov['obs_ws'] * (np.cos((np.pi/180)*dfnov['obs_wd']))

dfdec['mod_zonal_wind'] = dfdec['mod_ws'] * (np.sin((np.pi/180)*dfdec['mod_wd']))
dfdec['mod_meridional_wind'] = dfdec['mod_ws'] * (np.cos((np.pi/180)*dfdec['mod_wd']))
dfdec['obs_zonal_wind'] = dfdec['obs_ws'] * (np.sin((np.pi/180)*dfdec['obs_wd']))
dfdec['obs_meridional_wind'] = dfdec['obs_ws'] * (np.cos((np.pi/180)*dfdec['obs_wd']))

dfjan['mod_zonal_wind'] = dfjan['mod_ws'] * (np.sin((np.pi/180)*dfjan['mod_wd']))
dfjan['mod_meridional_wind'] = dfjan['mod_ws'] * (np.cos((np.pi/180)*dfjan['mod_wd']))
dfjan['obs_zonal_wind'] = dfjan['obs_ws'] * (np.sin((np.pi/180)*dfjan['obs_wd']))
dfjan['obs_meridional_wind'] = dfjan['obs_ws'] * (np.cos((np.pi/180)*dfjan['obs_wd']))


df = pd.concat([dfnov, dfdec, dfjan], axis=0)


dfnov.tail()


## Plot wind speed overall for 3 months

# Plot of wind speed (2m) for Nov, Dec, Jan

fig = plt.figure(figsize=(20,15))
plt.subplot(311)
plt.plot(dfnov.index, dfnov['mod_ws'], label='model', color='red', ls='solid')
plt.plot(dfnov.index, dfnov['mod_ws'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfnov.index, dfnov['obs_ws'], label='observed', color='blue')
plt.plot(dfnov.index, dfnov['obs_ws'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('Wind Speed (in $m/s$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('November 2020', fontsize=18)

plt.subplot(312)
plt.plot(dfdec.index, dfdec['mod_ws'], label='model', color='red', ls='solid')
plt.plot(dfdec.index, dfdec['mod_ws'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfdec.index, dfdec['obs_ws'], label='observed', color='blue')
plt.plot(dfdec.index, dfdec['obs_ws'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('Wind Speed (in $m/s$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('December 2020', fontsize=18)

plt.subplot(313)
plt.plot(dfjan.index, dfjan['mod_ws'], label='model', color='red', ls='solid')
plt.plot(dfjan.index, dfjan['mod_ws'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfjan.index, dfjan['obs_ws'], label='observed', color='blue')
plt.plot(dfjan.index, dfjan['obs_ws'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('Wind Speed (in $m/s$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('January 2021', fontsize=18)

fig.suptitle('Wind Speed in Delhi', fontsize=24, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/ws.png')


fig = plt.figure(figsize=(15,15))

plt.subplot(311)
plt.scatter(dfnov['mod_zonal_wind'], dfnov['mod_meridional_wind'],color='red', alpha=0.35, label='model')
plt.scatter(dfnov['obs_zonal_wind'], dfnov['obs_meridional_wind'],color='blue', alpha=0.35, label='observed')
plt.ylabel('Meridional wind speed (in $m/s$)', fontsize=14)
plt.xlabel('Zonal wind speed in $m/s$)', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('November 2020', fontsize=18)

plt.subplot(312)
plt.scatter(dfdec['mod_zonal_wind'], dfdec['mod_meridional_wind'],color='red', alpha=0.35, label='model')
plt.scatter(dfdec['obs_zonal_wind'], dfdec['obs_meridional_wind'],color='blue', alpha=0.35, label='observed')
plt.ylabel('Meridional wind speed (in $m/s$)', fontsize=14)
plt.xlabel('Zonal wind speed in $m/s$)', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('December 2020', fontsize=18)

plt.subplot(313)
plt.scatter(dfjan['mod_zonal_wind'], dfjan['mod_meridional_wind'],color='red', alpha=0.35, label='model')
plt.scatter(dfjan['obs_zonal_wind'], dfjan['obs_meridional_wind'],color='blue', alpha=0.35, label='observed')
plt.ylabel('Meridional wind speed (in $m/s$)', fontsize=14)
plt.xlabel('Zonal wind speed in $m/s$)', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('January 2021', fontsize=18)

fig.suptitle('Zonal and Meriodional Wind Speed distribution in Delhi', fontsize=24, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/z_m_ws.png')


# Diurnal variation

fig = plt.figure(figsize=(15,15))

plt.subplot(411)
dplot.plot(df, df, 'mod_ws', 'obs_ws')
plt.ylabel('Wind Speed', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('NDJ Combined', fontsize=18)

plt.subplot(412)
dplot.plot(dfnov, dfnov, 'mod_ws', 'obs_ws')
plt.ylabel('Wind Speed', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('November 2020', fontsize=18)

plt.subplot(413)
dplot.plot(dfdec, dfdec, 'mod_ws', 'obs_ws')
plt.ylabel('Wind Speed', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('December 2020', fontsize=18)

plt.subplot(414)
dplot.plot(dfjan, dfjan, 'mod_ws', 'obs_ws')
plt.ylabel('Wind Speed', fontsize=14)
plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('January 2021', fontsize=18)

fig.suptitle('Wind Speed (in $m/s$) over Delhi', fontsize=24, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/diurnal_ws.png')


# Diurnal variation Zonal Wind

fig = plt.figure(figsize=(15,15))

plt.subplot(411)
dplot.plot(df, df, 'mod_zonal_wind', 'obs_zonal_wind')
plt.ylabel('Wind Speed', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('NDJ Combined', fontsize=18)

plt.subplot(412)
dplot.plot(dfnov, dfnov, 'mod_zonal_wind', 'obs_zonal_wind')
plt.ylabel('Wind Speed', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('November 2020', fontsize=18)

plt.subplot(413)
dplot.plot(dfdec, dfdec, 'mod_zonal_wind', 'obs_zonal_wind')
plt.ylabel('Wind Speed', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('December 2020', fontsize=18)

plt.subplot(414)
dplot.plot(dfjan, dfjan, 'mod_zonal_wind', 'obs_zonal_wind')
plt.ylabel('Wind Speed', fontsize=14)
plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('January 2021', fontsize=18)

fig.suptitle('Zonal Wind Speed (in $m/s$) over Delhi', fontsize=24, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/diurnal_zonal_wind.png')


# Diurnal variation meridional Wind

fig = plt.figure(figsize=(15,15))

plt.subplot(411)
dplot.plot(df, df, 'mod_meridional_wind', 'obs_meridional_wind')
plt.ylabel('Wind Speed', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('NDJ Combined', fontsize=18)

plt.subplot(412)
dplot.plot(dfnov, dfnov, 'mod_meridional_wind', 'obs_meridional_wind')
plt.ylabel('Wind Speed', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('November 2020', fontsize=18)

plt.subplot(413)
dplot.plot(dfdec, dfdec, 'mod_meridional_wind', 'obs_meridional_wind')
plt.ylabel('Wind Speed', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('December 2020', fontsize=18)

plt.subplot(414)
dplot.plot(dfjan, dfjan, 'mod_meridional_wind', 'obs_meridional_wind')
plt.ylabel('Wind Speed', fontsize=14)
plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('January 2021', fontsize=18)

fig.suptitle('Meridional Wind Speed (in $m/s$) over Delhi', fontsize=24, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/diurnal_meridional_wind.png')


ax = WindroseAxes.from_ax()
# ax.bar(dfnov.mod_wd, dfnov.mod_ws, normed=False, opening=0.8, edgecolor='white')
# coordinates = ('E','NE','N','NW','W','SW','S','SE')
# ax.set_xticklabels(coordinates)
# ax.set_legend()
# ax.set_title('November-Model', fontsize=14)

ax.box(dfnov.mod_wd, dfnov.mod_ws, bins=np.arange(0, 8, 1))
coordinates = ('E','NE','N','NW','W','SW','S','SE')
ax.set_xticklabels(coordinates)
ax.set_legend()
ax.set_title('November-Model', fontsize=14)

# ax.contourf(dfnov.mod_wd, dfnov.mod_ws, bins=np.arange(0, 8, 1), cmap=cm.hot)
# coordinates = ('E','NE','N','NW','W','SW','S','SE')
# ax.set_xticklabels(coordinates)
# ax.set_legend()
# ax.set_title('November-Model', fontsize=14)

Text(0.5, 1.0, 'November-Model')


fig=plt.figure(figsize=(12,12))

rect1=[0.5,1.5,0.4,0.4] 
wa1=WindroseAxes(fig, rect1)
fig.add_axes(wa1)
wa1.contourf(dfnov.mod_wd, dfnov.mod_ws, bins=np.arange(0, 8, 1), cmap=cm.Reds)
coordinates = ('E','NE','N','NW','W','SW','S','SE')
wa1.set_xticklabels(coordinates)
wa1.set_legend()
wa1.set_title('November 2020 - Model', fontsize=14)

rect2 = [1.0,1.5,0.4,0.4]
wa2=WindroseAxes(fig, rect2)
fig.add_axes(wa2)
wa2.contourf(dfnov.obs_wd, dfnov.obs_ws, bins=np.arange(0, 7, 1), cmap=cm.Blues)
coordinates = ('E','NE','N','NW','W','SW','S','SE')
wa2.set_xticklabels(coordinates)
wa2.set_legend()
wa2.set_title('November 2020 - Observation', fontsize=14)

rect3=[0.5,1.0,0.4,0.4] 
wa3=WindroseAxes(fig, rect3)
fig.add_axes(wa3)
wa3.contourf(dfdec.mod_wd, dfdec.mod_ws, bins=np.arange(0, 6, 1), cmap=cm.Reds)
coordinates = ('E','NE','N','NW','W','SW','S','SE')
wa3.set_xticklabels(coordinates)
wa3.set_legend()
wa3.set_title('December 2020 - Model', fontsize=14)

rect4 = [1.0,1.0,0.4,0.4]
wa4=WindroseAxes(fig, rect4)
fig.add_axes(wa4)
wa4.contourf(dfdec.obs_wd, dfdec.obs_ws, bins=np.arange(0, 6, 1), cmap=cm.Blues)
coordinates = ('E','NE','N','NW','W','SW','S','SE')
wa4.set_xticklabels(coordinates)
wa4.set_legend()
wa4.set_title('December 2020 - Observation', fontsize=14)

rect5=[0.5,0.5,0.4,0.4] 
wa5=WindroseAxes(fig, rect5)
fig.add_axes(wa5)
wa5.contourf(dfjan.mod_wd, dfjan.mod_ws, bins=np.arange(0, 7, 1), cmap=cm.Reds)
coordinates = ('E','NE','N','NW','W','SW','S','SE')
wa5.set_xticklabels(coordinates)
wa5.set_legend()
wa5.set_title('January 2021 - Model', fontsize=14)

rect6 = [1.0,0.5,0.4,0.4]
wa6=WindroseAxes(fig, rect6)
fig.add_axes(wa6)
wa6.contourf(dfjan.obs_wd, dfjan.obs_ws, bins=np.arange(0, 7, 1), cmap=cm.Blues)
coordinates = ('E','NE','N','NW','W','SW','S','SE')
wa6.set_xticklabels(coordinates)
wa6.set_legend()
wa6.set_title('January 2021 - Observation', fontsize=14)

fig.suptitle('Wind Rose Delhi', fontsize=24, y=2.0)
# plt.savefig('./images/met_data/wind_rose.png') 

##### Manualy save the image ##########

Text(0.5, 2.0, 'Wind Rose Delhi')


## Plot Downwelling Short-wave radiation for 3 months

fig = plt.figure(figsize=(20,15))
plt.subplot(311)
plt.plot(dfnov.index, dfnov['mod_swdown'], label='model', color='red', ls='solid')
plt.plot(dfnov.index, dfnov['mod_swdown'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfnov.index, dfnov['obs_swupper'], label='observed', color='blue')
plt.plot(dfnov.index, dfnov['obs_swupper'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('Radiation (in $W/m^2$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('November 2020', fontsize=18)

plt.subplot(312)
plt.plot(dfdec.index, dfdec['mod_swdown'], label='model', color='red', ls='solid')
plt.plot(dfdec.index, dfdec['mod_swdown'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfdec.index, dfdec['obs_swupper'], label='observed', color='blue')
plt.plot(dfdec.index, dfdec['obs_swupper'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('Radiation (in $W/m^2$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('December 2020', fontsize=18)

plt.subplot(313)
plt.plot(dfjan.index, dfjan['mod_swdown'], label='model', color='red', ls='solid')
plt.plot(dfjan.index, dfjan['mod_swdown'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfjan.index, dfjan['obs_swupper'], label='observed', color='blue')
plt.plot(dfjan.index, dfjan['obs_swupper'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('Radiation (in $W/m^2$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('January 2021', fontsize=18)

fig.suptitle('Downwelling Shortwave Radiation in Delhi', fontsize=24, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/swdown.png')


## Plot Downwelling Short-wave radiation for 3 months with swlower

fig = plt.figure(figsize=(20,15))
plt.subplot(311)
plt.plot(dfnov.index, dfnov['mod_swdown'], label='model', color='red', ls='solid')
plt.plot(dfnov.index, dfnov['mod_swdown'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfnov.index, dfnov['obs_swlower'], label='observed', color='blue')
plt.plot(dfnov.index, dfnov['obs_swlower'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('Radiation (in $W/m^2$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('November 2020', fontsize=18)

plt.subplot(312)
plt.plot(dfdec.index, dfdec['mod_swdown'], label='model', color='red', ls='solid')
plt.plot(dfdec.index, dfdec['mod_swdown'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfdec.index, dfdec['obs_swlower'], label='observed', color='blue')
plt.plot(dfdec.index, dfdec['obs_swlower'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('Radiation (in $W/m^2$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('December 2020', fontsize=18)

plt.subplot(313)
plt.plot(dfjan.index, dfjan['mod_swdown'], label='model', color='red', ls='solid')
plt.plot(dfjan.index, dfjan['mod_swdown'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='red')
# plt.xticks(rotation= 10)
plt.plot(dfjan.index, dfjan['obs_swlower'], label='observed', color='blue')
plt.plot(dfjan.index, dfjan['obs_swlower'].fillna(method='ffill'), ls = '--', lw = 1, label='missing data', color='blue')
#plt.xticks(rotation= 10)
plt.ylabel('Radiation (in $W/m^2$)', fontsize=14)
plt.xlabel('Time', fontsize=14)
plt.legend(loc=1, prop={'size': 14})
plt.title('January 2021', fontsize=18)

fig.suptitle('Downwelling Shortwave Radiation in Delhi', fontsize=24, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/swdown2.png')


# Diurnal variation

fig = plt.figure(figsize=(15,15))

plt.subplot(411)
dplot.plot(df, df, 'mod_swdown', 'obs_swupper')
plt.ylabel('Downwelling SW Radiation', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('NDJ Combined', fontsize=18)

plt.subplot(412)
dplot.plot(dfnov, dfnov, 'mod_swdown', 'obs_swupper')
plt.ylabel('Downwelling SW Radiation', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('November 2020', fontsize=18)

plt.subplot(413)
dplot.plot(dfdec, dfdec, 'mod_swdown', 'obs_swupper')
plt.ylabel('Downwelling SW Radiation', fontsize=14)
# plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('December 2020', fontsize=18)

plt.subplot(414)
dplot.plot(dfjan, dfjan, 'mod_swdown', 'obs_swupper')
plt.ylabel('Downwelling SW Radiation', fontsize=14)
plt.xlabel('Time (in hrs)', fontsize=14)
plt.title('January 2021', fontsize=18)

fig.suptitle('Downwelling Shortwave radiation (in $W/m^2$) over Delhi', fontsize=24, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/diurnal_swdown.png')


# Temperature
stat_calc.get_stat_performance(dfnov)

Mean bias :
mean bias 2.996370083056479
fractional bias is :
fractional bias =  0.13868509451208566
Correlation coefficient is :
corr coeff =  0.9559207775862428
RMSE is :
RMSE  3.210589484618314


# SWdown
stat_calc.get_stat_performance(dfnov)

Mean bias :
mean bias 35.31734086805557
fractional bias is :
fractional bias =  0.19005853424097413
Correlation coefficient is :
corr coeff =  0.9636219194670232
RMSE is :
RMSE  73.85228223109476


# WS
stat_calc.get_stat_performance(dfnov)

Mean bias :
mean bias 0.6184035034722216
fractional bias is :
fractional bias =  0.2903078692017948
Correlation coefficient is :
corr coeff =  0.6880553333999803
RMSE is :
RMSE  1.241016264602549


# WD
stat_calc.get_stat_performance(dfnov)

Mean bias :
mean bias 9.162879240506324
fractional bias is :
fractional bias =  0.03589719979777546
Correlation coefficient is :
corr coeff =  0.5611647650862172
RMSE is :
RMSE  90.02287647914856


# Temperature
stat_calc.get_stat_performance(dfdec)

Mean bias :
mean bias 2.9716905716612385
fractional bias is :
fractional bias =  0.18558600870646924
Correlation coefficient is :
corr coeff =  0.9607440219913479
RMSE is :
RMSE  3.2608752904015064


# SWdown
stat_calc.get_stat_performance(dfdec)

Mean bias :
mean bias 27.308171513605448
fractional bias is :
fractional bias =  0.14852163293970452
Correlation coefficient is :
corr coeff =  0.9641072394811453
RMSE is :
RMSE  54.84542482924812


# WS
stat_calc.get_stat_performance(dfdec)

Mean bias :
mean bias 0.24735524584040802
fractional bias is :
fractional bias =  0.10708826308880254
Correlation coefficient is :
corr coeff =  0.7107097217628047
RMSE is :
RMSE  0.8928116051719951


# WD
stat_calc.get_stat_performance(dfdec)

Mean bias :
mean bias 20.502105161290327
fractional bias is :
fractional bias =  0.0739338581501714
Correlation coefficient is :
corr coeff =  0.4123187095185867
RMSE is :
RMSE  87.86047439595785


# Temperature
stat_calc.get_stat_performance(dfjan)

Mean bias :
mean bias 3.5359927100632724
fractional bias is :
fractional bias =  0.22878140702568528
Correlation coefficient is :
corr coeff =  0.8043926122255016
RMSE is :
RMSE  4.129059355069105


# SWdown
stat_calc.get_stat_performance(dfjan)

Mean bias :
mean bias 40.54751516037326
fractional bias is :
fractional bias =  0.23517038161766263
Correlation coefficient is :
corr coeff =  0.8584217079201485
RMSE is :
RMSE  117.4553540667614


# WS
stat_calc.get_stat_performance(dfjan)

Mean bias :
mean bias 0.18347173469790665
fractional bias is :
fractional bias =  0.047970179508789
Correlation coefficient is :
corr coeff =  0.46399890571841745
RMSE is :
RMSE  1.3563548200271618


# WD
stat_calc.get_stat_performance(dfjan)

Mean bias :
mean bias 6.8747468374396234
fractional bias is :
fractional bias =  0.021512540798016296
Correlation coefficient is :
corr coeff =  0.5973089178922467
RMSE is :
RMSE  82.61426253692655


# Temperature
stat_calc.get_stat_performance(df)

Mean bias :
mean bias 3.175834805322422
fractional bias is :
fractional bias =  0.18470293402627572
Correlation coefficient is :
corr coeff =  0.9398379937914776
RMSE is :
RMSE  3.571918565003118


# SWdown
stat_calc.get_stat_performance(df)

Mean bias :
mean bias 34.49859949837076
fractional bias is :
fractional bias =  0.19304836952171214
Correlation coefficient is :
corr coeff =  0.9258103631612591
RMSE is :
RMSE  86.89308648603082


# WS
stat_calc.get_stat_performance(df)

Mean bias :
mean bias 0.3448088494106378
fractional bias is :
fractional bias =  0.13501402894364012
Correlation coefficient is :
corr coeff =  0.597368818909054
RMSE is :
RMSE  1.182899315682556


# WD
stat_calc.get_stat_performance(df)

Mean bias :
mean bias 12.145224019313675
fractional bias is :
fractional bias =  0.04548836440600391
Correlation coefficient is :
corr coeff =  0.5452696060663371
RMSE is :
RMSE  86.74729196978474


### Geting the individual dataframes for qqplot and rplot

temp = df[['mod_temp', 'obs_temp']]
temp_nov = dfnov[['mod_temp', 'obs_temp']]
temp_dec = dfdec[['mod_temp', 'obs_temp']]
temp_jan = dfjan[['mod_temp', 'obs_temp']]

swdown = df[['mod_swdown', 'obs_swupper']]
swdown_nov = dfnov[['mod_swdown', 'obs_swupper']]
swdown_dec = dfdec[['mod_swdown', 'obs_swupper']]
swdown_jan = dfjan[['mod_swdown', 'obs_swupper']]

wd = df[['mod_wd', 'obs_wd']]
wd_nov = dfnov[['mod_wd', 'obs_wd']]
wd_dec = dfdec[['mod_wd', 'obs_wd']]
wd_jan = dfjan[['mod_wd', 'obs_wd']]

ws = df[['mod_ws', 'obs_ws']]
ws_nov = dfnov[['mod_ws', 'obs_ws']]
ws_dec = dfdec[['mod_ws', 'obs_ws']]
ws_jan = dfjan[['mod_ws', 'obs_ws']]


# Temperature

fig = plt.figure(figsize=(20,10))

plt.subplot(141)
qqplot.plot(temp.dropna()['obs_temp'], temp.dropna()['mod_temp'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed temp', fontsize=16)
plt.ylabel('Model temp', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(142)
qqplot.plot(temp_nov.dropna()['obs_temp'], temp_nov.dropna()['mod_temp'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed temp', fontsize=16)
# plt.ylabel('Model temp', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(143)
qqplot.plot(temp_dec.dropna()['obs_temp'], temp_dec.dropna()['mod_temp'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed temp', fontsize=16)
# plt.ylabel('Model temp', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(144)
qqplot.plot(temp_jan.dropna()['obs_temp'], temp_jan.dropna()['mod_temp'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed temp', fontsize=16)
# plt.ylabel('Model temp', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Q-Q Plot for 2$m$ Temperature (in $^{o}C$) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/qqplot_temp.png')


# SWDOWN

fig = plt.figure(figsize=(20,10))

plt.subplot(141)
qqplot.plot(swdown.dropna()['obs_swupper'], swdown.dropna()['mod_swdown'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed swdown', fontsize=16)
plt.ylabel('Model swdown', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(142)
qqplot.plot(swdown_nov.dropna()['obs_swupper'], swdown_nov.dropna()['mod_swdown'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed swdown', fontsize=16)
# plt.ylabel('Model swdown', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(143)
qqplot.plot(swdown_dec.dropna()['obs_swupper'], swdown_dec.dropna()['mod_swdown'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed swdown', fontsize=16)
# plt.ylabel('Model swdown', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(144)
qqplot.plot(swdown_jan.dropna()['obs_swupper'], swdown_jan.dropna()['mod_swdown'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed swdown', fontsize=16)
# plt.ylabel('Model swdown', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Q-Q Plot for Downwelling shortwave radiation (in $W/m^2$) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/qqplot_swdown.png')


# ws

fig = plt.figure(figsize=(20,10))

plt.subplot(141)
qqplot.plot(ws.dropna()['obs_ws'], ws.dropna()['mod_ws'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed ws', fontsize=16)
plt.ylabel('Model ws', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(142)
qqplot.plot(ws_nov.dropna()['obs_ws'], ws_nov.dropna()['mod_ws'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed ws', fontsize=16)
# plt.ylabel('Model ws', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(143)
qqplot.plot(ws_dec.dropna()['obs_ws'], ws_dec.dropna()['mod_ws'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed ws', fontsize=16)
# plt.ylabel('Model ws', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(144)
qqplot.plot(ws_jan.dropna()['obs_ws'], ws_jan.dropna()['mod_ws'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed ws', fontsize=16)
# plt.ylabel('Model ws', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Q-Q Plot for Wind speed (in $m/s$) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/qqplot_ws.png')


# wd

fig = plt.figure(figsize=(20,10))

plt.subplot(141)
qqplot.plot(wd.dropna()['obs_wd'], wd.dropna()['mod_wd'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed wd', fontsize=16)
plt.ylabel('Model wd', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(142)
qqplot.plot(wd_nov.dropna()['obs_wd'], wd_nov.dropna()['mod_wd'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed wd', fontsize=16)
# plt.ylabel('Model wd', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(143)
qqplot.plot(wd_dec.dropna()['obs_wd'], wd_dec.dropna()['mod_wd'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed wd', fontsize=16)
# plt.ylabel('Model wd', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(144)
qqplot.plot(wd_jan.dropna()['obs_wd'], wd_jan.dropna()['mod_wd'], c='darkorange', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed wd', fontsize=16)
# plt.ylabel('Model wd', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Q-Q Plot for Wind Direction (in deg) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/qqplot_wd.png')


# Temperature

fig = plt.figure(figsize=(20,20))

plt.subplot(221)
rplot.plot(temp, temp.dropna()['obs_temp'], temp.dropna()['mod_temp'], color='k', alpha=0.7)
# plt.xlabel('Observed temp', fontsize=16)
plt.ylabel('Model temp', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(222)
rplot.plot(temp_nov, temp_nov.dropna()['obs_temp'], temp_nov.dropna()['mod_temp'], color='k', alpha=0.7)
# plt.xlabel('Observed temp', fontsize=16)
plt.ylabel('Model temp', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(223)
rplot.plot(temp_dec, temp_dec.dropna()['obs_temp'], temp_dec.dropna()['mod_temp'], color='k', alpha=0.7)
plt.xlabel('Observed temp', fontsize=16)
plt.ylabel('Model temp', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(224)
rplot.plot(temp_jan, temp_jan.dropna()['obs_temp'], temp_jan.dropna()['mod_temp'], color='k', alpha=0.7)
plt.xlabel('Observed temp', fontsize=16)
plt.ylabel('Model temp', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Scatter Plots for 2$m$ Temperature (in $^{o}C$) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/rplot_temp.png')


# Downwelling Shortwave radiation

fig = plt.figure(figsize=(20,20))

plt.subplot(221)
rplot.plot(swdown, swdown.dropna()['obs_swupper'], swdown.dropna()['mod_swdown'], color='k', alpha=0.7)
# plt.xlabel('Observed swdown', fontsize=16)
plt.ylabel('Model swdown', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(222)
rplot.plot(swdown_nov, swdown_nov.dropna()['obs_swupper'], swdown_nov.dropna()['mod_swdown'], color='k', alpha=0.7)
# plt.xlabel('Observed swdown', fontsize=16)
plt.ylabel('Model swdown', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(223)
rplot.plot(swdown_dec, swdown_dec.dropna()['obs_swupper'], swdown_dec.dropna()['mod_swdown'], color='k', alpha=0.7)
plt.xlabel('Observed swdown', fontsize=16)
plt.ylabel('Model swdown', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(224)
rplot.plot(swdown_jan, swdown_jan.dropna()['obs_swupper'], swdown_jan.dropna()['mod_swdown'], color='k', alpha=0.7)
plt.xlabel('Observed swdown', fontsize=16)
plt.ylabel('Model swdown', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Scatter Plots for Downwelling shortwave radiation (in $W/m^2$) in Delhi', fontsize=27, y=0.99)
fig.suptitle('for 2$m$ Downwelling Shortwave radiation (in $^{o}C$) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/rplot_swdown.png')


# Wind Speed

fig = plt.figure(figsize=(20,20))

plt.subplot(221)
rplot.plot(ws, ws.dropna()['obs_ws'], ws.dropna()['mod_ws'], color='k', alpha=0.7)
# plt.xlabel('Observed ws', fontsize=16)
plt.ylabel('Model ws', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(222)
rplot.plot(ws_nov, ws_nov.dropna()['obs_ws'], ws_nov.dropna()['mod_ws'], color='k', alpha=0.7)
# plt.xlabel('Observed ws', fontsize=16)
plt.ylabel('Model ws', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(223)
rplot.plot(ws_dec, ws_dec.dropna()['obs_ws'], ws_dec.dropna()['mod_ws'], color='k', alpha=0.7)
plt.xlabel('Observed ws', fontsize=16)
plt.ylabel('Model ws', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(224)
rplot.plot(ws_jan, ws_jan.dropna()['obs_ws'], ws_jan.dropna()['mod_ws'], color='k', alpha=0.7)
plt.xlabel('Observed ws', fontsize=16)
plt.ylabel('Model ws', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Scatter Plots for Wind speed (in $m/s$) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/rplot_ws.png')


# Wind Direction

fig = plt.figure(figsize=(20,20))

plt.subplot(221)
rplot.plot(wd, wd.dropna()['obs_wd'], wd.dropna()['mod_wd'], color='k', alpha=0.7)
# plt.xlabel('Observed wd', fontsize=16)
plt.ylabel('Model wd', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(222)
rplot.plot(wd_nov, wd_nov.dropna()['obs_wd'], wd_nov.dropna()['mod_wd'], color='k', alpha=0.7)
# plt.xlabel('Observed wd', fontsize=16)
plt.ylabel('Model wd', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(223)
rplot.plot(wd_dec, wd_dec.dropna()['obs_wd'], wd_dec.dropna()['mod_wd'], color='k', alpha=0.7)
plt.xlabel('Observed wd', fontsize=16)
plt.ylabel('Model wd', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(224)
rplot.plot(wd_jan, wd_jan.dropna()['obs_wd'], wd_jan.dropna()['mod_wd'], color='k', alpha=0.7)
plt.xlabel('Observed wd', fontsize=16)
plt.ylabel('Model wd', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Scatter Plots for Wind Direction (in deg) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_data/rplot_wd.png')

	date	hour	mod_temp	obs_temp	mod_wd	obs_wd	mod_ws	obs_ws	mod_swdown	obs_swlower	obs_swupper
0	2020-11-3	9:00:00	30.50861	28.81444	NaN	308.13890	1.75760	4.513278	0.00000	75.140	452.300
1	2020-11-3	10:00:00	30.16214	28.72625	NaN	321.01250	4.75277	3.983250	380.66110	52.560	284.700
2	2020-11-3	11:00:00	29.53625	27.90080	NaN	320.15200	3.71238	2.345093	174.24031	19.560	74.820
3	2020-11-3	12:00:00	28.43674	26.85611	NaN	152.76110	3.51142	2.113319	6.66098	9.130	8.840
4	2020-11-3	13:00:00	27.59320	25.19833	NaN	74.14445	1.84325	1.242681	0.00000	6.859	0.391

	time	mod_temp	obs_temp	mod_wd	obs_wd	mod_ws	obs_ws	mod_swdown	obs_swlower	obs_swupper
0	12/1/2020 0:00:00	16.35733	13.36347	248.06122	274.7847	1.47951	1.486333	0.00000	4.649	-2.333
1	12/1/2020 1:00:00	16.04544	12.92195	251.86467	255.9778	1.42542	0.880125	0.00000	4.458	-0.119
2	12/1/2020 2:00:00	15.97870	13.14375	253.92874	263.9764	1.24226	1.098139	35.57781	9.950	40.920
3	12/1/2020 3:00:00	17.47351	14.14000	255.25107	281.9695	1.29906	1.502556	203.14876	21.760	164.200
4	12/1/2020 4:00:00	19.46429	16.18889	268.71393	266.4000	1.23652	1.776250	382.20981	34.140	314.500

	datetime	mod_temp	obs_temp	mod_wd	obs_wd	mod_ws	obs_ws	mod_swdown	obs_swlower	obs_swupper	obs_lwupper	obs_lwlower
0	2021-01-01 0:00:00	NaN	6.682222	NaN	79.756946	NaN	0.900347	NaN	3.510417	-0.786550	618.516667	620.381667
1	2021-01-01 1:00:00	NaN	6.623194	NaN	107.330557	NaN	1.106306	NaN	4.742067	15.913250	615.495000	617.031667
2	2021-01-01 2:00:00	NaN	7.471944	NaN	112.248613	NaN	0.464417	NaN	12.202367	106.011333	587.153333	589.123333
3	2021-01-01 3:00:00	NaN	10.370833	NaN	104.988888	NaN	0.823708	NaN	23.618167	257.441667	524.011667	544.338333
4	2021-01-01 4:00:00	NaN	13.849861	NaN	138.116665	NaN	1.378944	NaN	37.442667	379.663333	458.161667	504.655000

	mod_temp	obs_temp	mod_wd	obs_wd	mod_ws	obs_ws	obs_swlower	obs_swupper	time	mod_zonal_wind	mod_meridional_wind	obs_zonal_wind	obs_meridional_wind
2020-11-30 19:00:00	18.75638	15.13005	236.81689	241.8534	1.30697	0.301540	4.078	1.030	19:00:00	-1.093837	-0.715326	-0.265881	-0.142245
2020-11-30 20:00:00	18.29080	14.59389	235.96709	225.8611	1.45559	0.487917	3.804	0.937	20:00:00	-1.206271	-0.814649	-0.350155	-0.339785
2020-11-30 21:00:00	17.77780	14.18430	243.52698	273.3250	1.66242	0.650542	5.002	0.066	21:00:00	-1.488106	-0.741068	-0.649447	0.037731
2020-11-30 22:00:00	17.23276	13.96889	246.77043	250.8000	1.68462	0.632292	4.403	0.442	22:00:00	-1.548051	-0.664441	-0.597121	-0.207940
2020-11-30 23:00:00	16.74167	NaN	246.57153	NaN	1.59641	NaN	NaN	NaN	23:00:00	-1.464797	-0.634739	NaN	NaN

Meteorological data analysis for Delhi (SRFP 2021)¶

Plotting¶

Temperature¶

Wind Speed¶

Distribution of zonal and meridional wind¶

Windrose plots for wind direction¶

SW radiation plot (Downward)¶

Calculation of statistical performance¶

November¶

December¶

January¶

Combined¶

QQ-plot¶

Scatter-correlation plot¶